/ Merritt Data

The Value of Quality Control

Merritt Research has developed strenuous quality control procedures throughout the data gathering, extraction, entry, review and grooming processes. We believe that the reputation of our data relies heavily on the quality control procedures that we enact. The following sections detail how quality control is instilled in each data process.

Data Gathering

With our audit request letters we send a survey requesting key industry accepted operating statistics. To ensure consistency, we include a set of definitions that functions as a guideline for the borrower to use when completing the survey. These steps ensure that our clients are receiving quality operational statistics from which accurate trends can be derived.

Data Entry

Several data checks are performed when entering the prepared data into the database. An Entry Analyst is the first team member to prepare and extract the data to spreadsheet. During the preparation and extraction process, the analyst will consult the internal procedures manual which has been developed to ensure that every team member is applying like standards. To promote data quality and consistency, the manual contains guidelines on the extraction of every data field in our database.

The Review Process

To ensure quality control, a senior team member or Review Analyst will review every data field that is entered by another team member. To guarantee continuity, the review process mirrors the initial extraction efforts. The Review Analyst pours through the same document and process as the original Entry Analyst and identifies any discrepancies. If a discrepancy is found, it is discussed and referenced against the procedures manual to arrive at the most appropriate answer. We place great emphasis on the review process, ensuring that our clients receive only the most accurate and objective data.

Automated Data Audit

Merritt Research’s final quality control procedure utilizes reports to identify mathematically improbable or impossible credit scenarios. The intention of this monthly data audit is to create a final data check to screen and account for any financial irregularities. This process includes a sequence of more than 75 reports to identify potentially erroneous data. Any data inconsistencies are catalogued and then manually reviewed. This final process of identifying and evaluating uncharacteristic data points serves as an extra level of assurance that our data is thoroughly and methodically examined.